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Sell More, Pay Less: Drive Conversions
with Unrelated Keywords
EXECUTIVE SUMMARY
How do you know what product sales are driven from which keywords within
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your paid search efforts?
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Which keywords are driving the most profitable traffic?
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Are you doing everything to maximize your paid search ROI?
WebTrends Sales
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A common perception within the online advertising community is that a successful
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ad sells product it was intended to sell. Surprisingly, that is not always the case.
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In reality, online conversions often originate from traffic generated by keywords
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that are entirely unrelated to the items eventually purchased. For instance, a large
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percentage of fur coats on a merchant’s site were purchased by people who
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searched on “gold watches”. Who would have thought gold watches and fur coats
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are related?
Are these conversions completely random, or do trends exist within them that can
be leveraged to maximize your online revenue? Read on and find out for yourself.
Online conversions often originate from traffic generated by search engine terms
that are entirely unrelated to the items eventually purchased. Optimal keyword
management recognizes these relationships, automatically allocating spend among
those terms that convert at the target profit goal.
WebTrends conducted a study to understand these relationships. The goal of the
study was to isolate the effect of paid search advertising for one product or type of
products on the sales of other products without results being skewed by branding
or search terms which could be deemed related to all categories. The data used
for this analysis consisted of 1,056 initial conversions that originated with generic
Google searches and paid advertisement clicks, performed over a period of three
months. The advertisements and purchases spanned several categories, including
home wares, technology, jewelry, wristwatches, fitness, clothing, and cosmetics.
RESULTS & DISCUSSION
Several challenges arise when analyzing this sort of complex data. The data tables
in this paper show the most interesting portions of the data collected and also
illustrate the challenges inherent in “cutting” data to reveal significant relationships.
A. The Data: The First Layer
The most basic question that the data answers is exactly how many of the 1,056
conversions originated from keywords unrelated to the products purchased. This

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relationship could not be calculated by simply summing the cross-category
conversions using the category distinctions assigned to advertisements and
products by the advertiser. Some categories like “Homewares” are far broader
than others like “Wristwatches.” Hence, a Wristwatch category purchase is
always going to be related to another purchase within the Wristwatch category,
but two Homewares purchases are not always going to be related. Due to this
lack of continuity, the method chosen was to label all cross-category conversions
as unrelated and then manually compare the query keyword to the product
purchased for all of the same-category conversions. These were the results:
Table 1: Related and Unrelated Conversions - Manual Keyword Comparison
As the table shows, a surprisingly large portion of conversions in this sample were
unrelated conversions. These results are quite contrary to the thinking that specific,
targeted keywords sell the products they advertise. Instead, this data indicates that
nearly half of generic conversions result from unrelated keywords.
B. The Data: The Second Layer
Next, it’s interesting to see how many conversions were cross-category
conversions:
Table 2: Cross-Category Conversions - Category Comparison
A quick comparison between Table 1 and Table 2 shows that there were 55
same-category unrelated conversions.
This represents 5.2% of the whole sample, a small enough percentage to ignore
for the benefits offered by using cross-category conversions as a proxy for
unrelated conversions, when searching for trends within the user behavior.
However, searching for such trends is not straightforward since the data shows
where conversions came from, but all of the categories saw different numbers of
clicks and conversions. For example, while 39% of Clothing ad clicks that
converted resulted in Clothing category product purchases, this 39% was actually
56% of Clothing purchases. These somewhat counterintuitive relationships are
seen in the two graphs shown here:
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Graph 1: Conversions by Purchased Item Category
Clicked Ad
100%
4% 5% Category
10% 11%
14%
5%
90% 14%
Wristwatches
34%
14%
11%
80% 6%
5%
Jewelry
70%
4%
10%
60% 60%
Housewares
18% 88%
59%
50%
Fitness
65% 10%
40% 70%
Technology
6%
30%
56%
10%
10%
Cosmetics
20%
14%
15%
10% 19% Clothing
11% 9%
5%
0%
Clothing Cosmetics Technology Fitness Housewares Jewelry Wristwatches
Purchased Item Category
While the relationships between Graph 1 and Graph 2 are somewhat
counterintuitive, there are several interesting trends that can be seen
within them.
Graph 2: Conversions by Clicked Ad Category
100% Purchased
8% 8%
10% 9% Item
13%
90% 16%
17%
15%
80% Wristwatches
26%
33%
70%
Jewelry
16% 73%
60%
6% Housewares
44%
50%
88%
12%
71% Fitness
39%
40%
39%
Technology
30%
4%
20% 39%
18% Cosmetics
15%
10% 11%
16%
Clothing
10%
7% 4%
0%
Clothing Cosmetics Technology Fitness Housewares Jewelry Wristwatches
Clicked Ad Category
The most obvious trend, in Graph 2, is the tendency of users to purchase within
the Jewelry category, no matter what category of advertisement was clicked. This
is a trend that would be expected within categories like Clothing, Wristwatches,
and perhaps even Cosmetics, due to the similarities in products and the people
who would tend to purchase within them. However, this trend is very surprising to
see within categories like Technology and Fitness. This phenomenon may be due
to a residual branding effect, where users who clicked on these dissimilar category
advertisements did so consciously or unconsciously knowing that there was a
robust jewelry selection on the site. It could also be an indicator of the preferences
of those who click paid search advertisements generally. However, no matter
what the source of the preference may be, this relationship could be valuable
if understood by the retailer, especially when examined alongside the average
revenue per conversion by category:
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Table 3: Weighted Average Revenue Per Conversion by Category
Specifically, since Jewelry is the category of goods most likely to be purchased on
impulse and is also the category with relatively high revenue per conversion, this
retailer may want to engineer their site so that jewelry is highly visible and easy
to purchase at all times. This simple change to the site would allow the merchant
to significantly increase revenue. To this end, the analysis is useful to illuminate
several flaws inherent in typical approaches to online ad spend management.
C. Typical Approach #1: Allocate Spend To Ads that Get Clicked
A common advertising methodology, and one encouraged by major search engines
like Google 1, is to direct spending towards advertisements that get clicked the
most. Advertisers in this camp interpret clicks on an ad to be affirmations of
the value of the particular advertisement. They assume that the traffic these ads
generate is the best traffic for their site, similar to the way traditional advertisers
would likely purchase magazine advertisements that generated the best response
from viewers.
This approach to advertising fails to fully harness the performance metrics
available to online advertisers. Clicks alone indicate traffic generation, but fail
to identify how much revenue the cost of the click generates for the advertiser.
Directing spend towards click traffic is a sub-optimal bid management method
when trying to generate profits from online advertising. Advertisers that allocate
advertising spend to just driving click traffic, but are blind to the quantity and value
of conversions, cannot accurately calculate how much each click is worth to them.
D. Typical Approach #2: Allocate Spend To Ads With Improving CTR
Another approach to advertising online, also encouraged by major search engines
like Google 2, is to orient advertising towards improving click-through rate. The
flaws in this approach are the same as those inherent in orienting campaigns
towards click volume. Without knowledge of what revenue a paid advertisement
click produces, it is impossible to value how much that particular click is worth
1 https://adwords.google.com/support/bin/static.py?page=tips.html
2 https://adwords.google.com/support/bin/static.py?page=tips.html
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and how much to pay for it. Furthermore, since advertisers generally pay for
clicks, not impressions, click-through rate should essentially be a non-factor 3 in
determining where to direct spend. Without a linkage between ad cost and ad
revenue, advertisers cannot make informed decisions regarding advertising spend.
E. Typical Approach #3: Allocate Spend Towards Products that Sell
Another approach employed in traditional advertising is to scale advertising
towards best-selling products. The basic thinking here is that the sales themselves
are good indicators of the factors driving revenues. Much like their brick and
mortar counterparts, some online retailers believe that the products sold on their
site indicate what is drawing traffic into their site in the first place. Most advertisers
who blindly scale advertising towards products that sell run a significant risk of
hurting the overall performance of their campaigns. Within this sample there were
240 conversions in the “Jewelry” category which represent 22.72% of the total
sales from the period, shown here:
A retailer basing his advertising spend on sales alone would potentially, as a
response to successful sales in the Jewelry category, increase Jewelry advertising
spend at the expense of categories that saw fewer conversions, such as “Clothing.”
However, this move can potentially hurt Jewelry performance because Clothing
advertisements actually produced 19.16% of the Jewelry conversions while Jewelry
ads only produced 17.91% of Jewelry conversions, a trend shown in Graph 1 and
Graph 2 shown previously.
Table 4: Clicked Ad Category and Purchased Item Category Breakdown
Thus, a reduction in advertising for a category like Cosmetics is very likely to
reduce Jewelry conversions and it is unclear if a proportionate increase in Jewelry
advertising would make up for the loss. The only way to know is through iterative
experimentation examining the relationship between specific ad spend and
specific sale revenue. An advertiser basing spend on revenue generation alone
is utilizing a sub-optimal approach because they are not factoring in conversions
generated from unrelated keywords and advertisements.
F. The Benefits of Profit-Based Optimization
The data and examples above illustrate the significance of the relationship between
ad cost and revenue generation, and the importance of using this data to measure
the return on ad spend (“ROAS” 4) for a particular ad. They also show that the
relationships between ad clicks and product sales are, on average, non-intuitive
and unrelated to the initial search query.
3 CTR may be a factor in the Google Quality Score, which impacts ad position and thus could potentially be a factor
in campaign decisions.
4 ROAS = Revenue Generated from Advertising/Cost of Advertising.
5